Automatically determining semantic relations in Chinese sentences

After parsing an important task is to determine the semantic structure of sentences. In this paper, we attempt to automatically annotate the Penn Chinese Treebank with semantic dependency structure. Initially a small portion of the Penn Chinese Treebank was manually annotated with headword and dependency relations. Two supervised machine learning algorithms with varying features were then used to learn the relations. Finally, a set of rules were created based on features of Chinese to solve some problem patterns that were found in the Penn Chinese Treebank dealing with ambiguous structures. The experimental results show that the algorithms and proposed approach are effective for determining semantic dependency structure automatically.